Although clinically highly relevant, physiology has remained a systems and macroscopic embodiment of scientific thought separate from the molecular basis of genetics. The physiogenomics method of the present invention bridges the gap between the systems approach and the genomic approach by using human variability in physiological processes, either in health or disease, to drive their understanding at the genome level. Physiogenomics is particularly relevant to the phenotypes of complex diseases and the clustering of phenotypes into domains according to measurement technique, ranging from functional imaging and clinical scales to protein serology and gene expression.
Physiogenomics integrates genotypes, phenotypes and population analysis of functional variability among individuals. In physiogenomics, allelic genetic markers (single nucleotide polymorphisms or “SNPs”, haplotypes, insertion/deletions, tandem repeats) are analyzed to discover statistical associations to physiological characteristics in populations of individuals either at baseline or after they have been similarly exposed or challenged to environmental triggers. These environmental challenges span the gamut from exercise and diet to drugs and toxins, and from extremes of temperature, pressure and altitude to radiation. In the case of complex diseases we are likely to find both baseline characteristics and response phenotypes to as yet undetermined environmental triggers. Variability in a genomic marker among individuals that tracks with the variability in physiological characteristics establishes associations and mechanistic links with specific genes.
Physiogenomics is a medical application of engineering sensitivity analysis [see, e.g., G. Ruano, A. Windemuth, and T. Holford: “Physiogenomics: Integrating systems engineering and nanotechnology for personalized health”, The Biomedical Engineering Handbook, 3rd Edition, CRC Press 2006; T. R. Holford, A. Windemuth, and G. Ruano, “Personalizing public health”, Personalized Medicine, 2(3), 2005; and A. Saltelli, K. Chan, and E. M. Scott “Sensitivity Analysis”, John Wiley and Sons, Chichester, 2000]. Sensitivity analysis is the study about the relations between the input and the output of a model and the analysis utilizing systems theory, of how variation of the input leads to changes in the output quantities. Physiogenomics integrates systems engineering with molecular probes stemming from genomic markers available from industrial technologies. Physiogenomics utilizes as input the variability in genes and relates the genetic variability to variability in the physiological characteristics, which is the output. As a non-limiting example, the genetic variability may be measured by the frequency of single nucleotide polymorphisms (SNPs). With physiogenomics, ensembles of 105 to 106 SNP markers can be integrated with population analysis of functional variability among individuals similarly treated [T. R. Holford, A. Windemuth, and G. Ruano, “Personalizing public health”, Personalized Medicine, 2(3), 2005]. Variability in SNP frequency among individuals, which tracks with variability in physiological characteristics, establishes genetic associations and mechanistic links with specific genes.
The physiogenomic method of the invention marks the entry of genomics into systems biology and requires novel analytical platforms to integrate the data and derive the most robust associations. Once physiological systems are under scrutiny, the industrial tools of high-throughput genomics do not suffice, as fundamentals processes such as signal amplification, functional reserve and feedback loops of homeostasis must be incorporated.
The inventive physiogenomics method includes marker discovery and model building. Each of these interrelated components will be described in a generic fashion. Reduction to practice of the generic physiogenomic invention will then be demonstrated by our experimental data in the Examples section.